Modeling sub-boreal forest canopy bulk density in Minnesota, USA, using synthetic aperture radar and optical satellite sensor data

Background National estimates of canopy bulk density (CBD; kg m−3) for fire behavior modeling are generated and supported by the LANDFIRE program. However, locally derived estimates of CBD at finer scales are preferred over national estimates if they exist, as the absolute accuracy of the LANDFIRE CBD product is low and varies regionally. Active sensors (e.g., lidar or radar) are better suited for this task, as passive sensors are ill equipped to detect differences among key vertical fuel structures, such as coniferous surface fuels (≤2 m high) and canopy fuels above this threshold—a key categorical fuel distinction in fire behavior modeling. However, previous efforts to map CBD using lidar sensor data in the Superior National Forest (SNF) of Minnesota, USA, yielded substandard results. Therefore, we use a combination of dormant-season synthetic aperture radar (SAR) and optical satellite sensor data to (1) expand detectability of coniferous fuels among mixed forest canopies to improve the accuracy of CBD modeling and (2) better understand the influence of surface fuels in this regard. Response variables included FuelCalc output and indirect estimates of maximum burnable fuel based on canopy gap fraction (CGF) measured at ground level and 2 m above ground level. Results SAR variables were important predictors of CBD and total fuel density (TFD) in all independent model calibrations with ground data, in which we define TFD as the sum of CBD and primarily live coniferous surface fuel density (SFD) 0 to 2 m above ground. Exploratory estimates of TFD appeared biased to the presence of sapling-stage conifer fuel on measures of CGF at the ground level. Thus, modeling efforts to calibrate SFD with satellite sensor data failed. Both CGF-based and FuelCalc-based field estimates of CBD yielded close unity with satellite-calibrated estimates, although substantial differences in data distributions existed. Estimates of CBD from the widest CGF zenith angle range (0 to 38°) correlated best with FuelCalc-based CBD estimates, while both resulted in maximum biomass values that exceeded those considered typical for the SNF. Model results from the narrowest zenith angle range (0 to 7°) produced estimates of CBD that were more in line with values considered typical. LANDFIRE’s estimates of CBD were weakly, but significantly (P = 0.05), correlated to both narrow- and wide-angle CGF-based estimates of CBD, but not with FuelCalc-based estimates. Conclusions The combined use of field estimates of CBD, based on indirect measures of CGF according to Keane et al. (Canadian Journal of Forest Research 35:724–739, 2005), with SAR and optical satellite sensor data demonstrates the potential of this method for mapping CBD in the Upper Midwest, USA. Results suggested that the presence of live, coniferous surface fuels neither confounds remote detection nor precludes mapping of CBD in this region using SAR satellite sensor data, as C- and L-band idiosyncrasies likely limit the visibility of these smaller understory fuels from space. Nevertheless, research using direct measures of burnable SFD for calibrations with SAR satellite sensor data should be conducted to more definitively answer this remote detection question, as we suspect substantial bias among measures of CGF from ground level when estimating SFD as the difference between TFD and CBD.

Observed versus predicted fire behavior in an Alaskan black spruce forest ecosystem: an experimental fire case study

Background Fire managers tasked with assessing the hazard and risk of wildfire in Alaska, USA, tend to have more confidence in fire behavior prediction modeling systems developed in Canada than similar systems developed in the US. In 1992, Canadian fire behavior systems were adopted for modeling fire hazard and risk in Alaska and are used by fire suppression specialists and fire planners working within the state. However, as new US-based fire behavior modeling tools are developed, Alaskan fire managers are encouraged to adopt the use of US-based systems. Few studies exist in the scientific literature that inform fire managers as to the efficacy of fire behavior modeling tools in Alaska. In this study, I provide information to aid fire managers when tasked with deciding which system for modeling fire behavior is most appropriate for their use. On the Magitchlie Creek Fire in Alaska, I systematically collected fire behavior characteristics within a black spruce (Picea mariana [Mill.] Britton, Sterns & Poggenb.) ecosystem under head fire conditions. I compared my fire behavior observations including flame length, rate of spread, and head fire intensity with fire behavior predictions from the US fire modeling system BehavePlus, and three Canadian systems: RedAPP, CanFIRE, and the Crown Fire Initiation and Spread system (CFIS). Results All four modeling systems produced reasonable rate of spread predictions although the Canadian systems provided predictions slightly closer to the observed fire behavior. The Canadian fire behavior prediction modeling systems RedAPP and CanFIRE provided more accurate predictions of head fire intensity and fire type than BehavePlus or CFIS. Conclusions The most appropriate fire behavior modeling system for use in Alaskan black spruce ecosystems depends on what type of questions are being asked. For determining the rate of fire movement across a landscape, REDapp, CanFIRE, CFIS, or BehavePlus can all be expected to provide reasonably accurate estimates of rate of spread. If fire managers are interested in using predicted flame length or energy produced for informing decisions such as which firefighting tactics will be successful, or for evaluating the ecological impacts due to burning, then the Canadian fire modeling systems outperformed BehavePlus in this case study.